




'''simple docstring'''



import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class 						lowercase_       (					A	,    unittest.TestCase  ):


   """simple docstring"""

   lowerCamelCase_							    =		MgpstrTokenizer
   lowerCamelCase_							    =		False
   lowerCamelCase_							    =		{}
   lowerCamelCase_							    =		False



   def 				lowerCAmelCase_     (       self   :      Tuple       ):

      """simple docstring"""
      super().setUp()

      # fmt: off
      _SCREAMING_SNAKE_CASE			= ["[GO]", "[s]", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z"]
      # fmt: on
      _SCREAMING_SNAKE_CASE			= dict(zip(__lowerCamelCase    , range(len(__lowerCamelCase       )       )       )       )

      _SCREAMING_SNAKE_CASE			= os.path.join(self.tmpdirname    , VOCAB_FILES_NAMES["vocab_file"]       )
      with open(self.vocab_file    , "w"    , encoding="utf-8"       ) as fp:
         fp.write(json.dumps(__lowerCamelCase       ) + "\n"       )



   def 				lowerCAmelCase_     (       self   :      Any    , **__lowerCamelCase   :      Optional[int]       ):

      """simple docstring"""
      return MgpstrTokenizer.from_pretrained(self.tmpdirname    , **__lowerCamelCase       )



   def 				lowerCAmelCase_     (       self   :      int    , __lowerCamelCase   :      int       ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE			= "tester"
      _SCREAMING_SNAKE_CASE			= "tester"
      return input_text, output_text



   @unittest.skip("MGP-STR always lower cases letters."       )
   def 				lowerCAmelCase_     (       self   :      Tuple       ):

      """simple docstring"""
      pass



   def 				lowerCAmelCase_     (       self   :      Optional[Any]       ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE			= self.get_tokenizers(do_lower_case=__lowerCamelCase       )
      for tokenizer in tokenizers:
         with self.subTest(F"""{tokenizer.__class__.__name__}"""       ):
            _SCREAMING_SNAKE_CASE			= "[SPECIAL_TOKEN]"

            tokenizer.add_special_tokens({"cls_token": special_token}       )
            _SCREAMING_SNAKE_CASE			= tokenizer.encode([special_token]    , add_special_tokens=__lowerCamelCase       )
            self.assertEqual(len(__lowerCamelCase       )    , 1       )

            _SCREAMING_SNAKE_CASE			= tokenizer.decode(__lowerCamelCase    , skip_special_tokens=__lowerCamelCase       )
            self.assertTrue(special_token not in decoded       )



   def 				lowerCAmelCase_     (       self   :      Any       ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE			= self.get_tokenizers()
      for tokenizer in tokenizers:
         with self.subTest(F"""{tokenizer.__class__.__name__}"""       ):
            _SCREAMING_SNAKE_CASE,			_SCREAMING_SNAKE_CASE			= self.get_input_output_texts(__lowerCamelCase       )

            _SCREAMING_SNAKE_CASE			= tokenizer.tokenize(__lowerCamelCase       )
            _SCREAMING_SNAKE_CASE			= tokenizer.convert_tokens_to_ids(__lowerCamelCase       )
            _SCREAMING_SNAKE_CASE			= tokenizer.encode(__lowerCamelCase    , add_special_tokens=__lowerCamelCase       )
            self.assertListEqual(__lowerCamelCase    , __lowerCamelCase       )

            _SCREAMING_SNAKE_CASE			= tokenizer.convert_ids_to_tokens(__lowerCamelCase       )
            self.assertNotEqual(len(__lowerCamelCase       )    , 0       )
            _SCREAMING_SNAKE_CASE			= tokenizer.decode(__lowerCamelCase       )
            self.assertIsInstance(__lowerCamelCase    , __lowerCamelCase       )

            self.assertEqual(text_a.replace(" "    , ""       )    , __lowerCamelCase       )



   @unittest.skip("MGP-STR tokenizer only handles one sequence."       )
   def 				lowerCAmelCase_     (       self   :      Optional[Any]       ):

      """simple docstring"""
      pass



   @unittest.skip("inputs cannot be pretokenized in MgpstrTokenizer"       )
   def 				lowerCAmelCase_     (       self   :      Tuple       ):

      """simple docstring"""
      pass





import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest







class 							lowerCamelCase__						(				UpperCAmelCase   ):






			"""simple docstring"""






			_UpperCamelCase		:       int 			=		(DDIMParallelScheduler,)
			_UpperCamelCase		:       List[Any] 			=		(('eta', 0.0), ('num_inference_steps', 50))




			def 					snake_case__					(				self   ,    **snake_case  ):

						'''simple docstring'''


						UpperCamelCase__       =							{
						    "num_train_timesteps": 1000,
						    "beta_start": 0.0001,
						    "beta_end": 0.02,
						    "beta_schedule": "linear",
						    "clip_sample": True,
						}

						config.update(**snake_case  )
						return config




			def 					snake_case__					(				self   ,    **snake_case  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.scheduler_classes[0]
						UpperCamelCase__       =							self.get_scheduler_config(**snake_case  )
						UpperCamelCase__       =							scheduler_class(**snake_case  )

						UpperCamelCase__,   UpperCamelCase__       =							10, 0.0

						UpperCamelCase__       =							self.dummy_model()
						UpperCamelCase__       =							self.dummy_sample_deter

						scheduler.set_timesteps(snake_case  )

						for t in scheduler.timesteps:
									UpperCamelCase__       =							model(snake_case   ,    snake_case  )
									UpperCamelCase__       =							scheduler.step(snake_case   ,    snake_case   ,    snake_case   ,    snake_case  ).prev_sample

						return sample




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						for timesteps in [100, 500, 1000]:
									self.check_over_configs(num_train_timesteps=snake_case  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						for steps_offset in [0, 1]:
									self.check_over_configs(steps_offset=snake_case  )

						UpperCamelCase__       =							self.scheduler_classes[0]
						UpperCamelCase__       =							self.get_scheduler_config(steps_offset=1  )
						UpperCamelCase__       =							scheduler_class(**snake_case  )
						scheduler.set_timesteps(5  )
						assert torch.equal(scheduler.timesteps   ,    torch.LongTensor([801, 601, 401, 201, 1]  )  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]   ,    [0.002, 0.02, 0.2, 2]  ):
									self.check_over_configs(beta_start=snake_case   ,    beta_end=snake_case  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						for schedule in ["linear", "squaredcos_cap_v2"]:
									self.check_over_configs(beta_schedule=snake_case  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						for prediction_type in ["epsilon", "v_prediction"]:
									self.check_over_configs(prediction_type=snake_case  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						for clip_sample in [True, False]:
									self.check_over_configs(clip_sample=snake_case  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						for timestep_spacing in ["trailing", "leading"]:
									self.check_over_configs(timestep_spacing=snake_case  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						for rescale_betas_zero_snr in [True, False]:
									self.check_over_configs(rescale_betas_zero_snr=snake_case  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						self.check_over_configs(thresholding=snake_case  )
						for threshold in [0.5, 1.0, 2.0]:
									for prediction_type in ["epsilon", "v_prediction"]:
												self.check_over_configs(
												    thresholding=snake_case   ,    prediction_type=snake_case   ,    sample_max_value=snake_case   ,    )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						for t in [1, 10, 49]:
									self.check_over_forward(time_step=snake_case  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						for t, num_inference_steps in zip([1, 10, 50]   ,    [10, 50, 500]  ):
									self.check_over_forward(time_step=snake_case   ,    num_inference_steps=snake_case  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						for t, eta in zip([1, 10, 49]   ,    [0.0, 0.5, 1.0]  ):
									self.check_over_forward(time_step=snake_case   ,    eta=snake_case  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.scheduler_classes[0]
						UpperCamelCase__       =							self.get_scheduler_config()
						UpperCamelCase__       =							scheduler_class(**snake_case  )

						assert torch.sum(torch.abs(scheduler._get_variance(0   ,    0  ) - 0.0  )  ) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(420   ,    400  ) - 0.14771  )  ) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(980   ,    960  ) - 0.32460  )  ) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(0   ,    0  ) - 0.0  )  ) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(487   ,    486  ) - 0.00979  )  ) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(999   ,    998  ) - 0.02  )  ) < 1E-5




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.scheduler_classes[0]
						UpperCamelCase__       =							self.get_scheduler_config()
						UpperCamelCase__       =							scheduler_class(**snake_case  )

						UpperCamelCase__,   UpperCamelCase__       =							10, 0.0
						scheduler.set_timesteps(snake_case  )

						UpperCamelCase__       =							self.dummy_model()
						UpperCamelCase__       =							self.dummy_sample_deter
						UpperCamelCase__       =							self.dummy_sample_deter + 0.1
						UpperCamelCase__       =							self.dummy_sample_deter - 0.1

						UpperCamelCase__       =							samplea.shape[0]
						UpperCamelCase__       =							torch.stack([samplea, samplea, samplea]   ,    dim=0  )
						UpperCamelCase__       =							torch.arange(snake_case  )[0:3, None].repeat(1   ,    snake_case  )

						UpperCamelCase__       =							model(samples.flatten(0   ,    1  )   ,    timesteps.flatten(0   ,    1  )  )
						UpperCamelCase__       =							scheduler.batch_step_no_noise(snake_case   ,    timesteps.flatten(0   ,    1  )   ,    samples.flatten(0   ,    1  )   ,    snake_case  )

						UpperCamelCase__       =							torch.sum(torch.abs(snake_case  )  )
						UpperCamelCase__       =							torch.mean(torch.abs(snake_case  )  )

						assert abs(result_sum.item() - 1147.7904  ) < 1E-2
						assert abs(result_mean.item() - 0.4982  ) < 1E-3




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.full_loop()

						UpperCamelCase__       =							torch.sum(torch.abs(snake_case  )  )
						UpperCamelCase__       =							torch.mean(torch.abs(snake_case  )  )

						assert abs(result_sum.item() - 172.0067  ) < 1E-2
						assert abs(result_mean.item() - 0.223967  ) < 1E-3




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.full_loop(prediction_type="v_prediction"  )

						UpperCamelCase__       =							torch.sum(torch.abs(snake_case  )  )
						UpperCamelCase__       =							torch.mean(torch.abs(snake_case  )  )

						assert abs(result_sum.item() - 52.5302  ) < 1E-2
						assert abs(result_mean.item() - 0.0684  ) < 1E-3




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.full_loop(set_alpha_to_one=snake_case   ,    beta_start=0.01  )
						UpperCamelCase__       =							torch.sum(torch.abs(snake_case  )  )
						UpperCamelCase__       =							torch.mean(torch.abs(snake_case  )  )

						assert abs(result_sum.item() - 149.8295  ) < 1E-2
						assert abs(result_mean.item() - 0.1951  ) < 1E-3







			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.full_loop(set_alpha_to_one=snake_case   ,    beta_start=0.01  )
						UpperCamelCase__       =							torch.sum(torch.abs(snake_case  )  )
						UpperCamelCase__       =							torch.mean(torch.abs(snake_case  )  )

						assert abs(result_sum.item() - 149.0784  ) < 1E-2
						assert abs(result_mean.item() - 0.1941  ) < 1E-3


